Robustness to nonstationarity has been a focus of a segment of the MAB literature that models rewards a choice picked by an adversary [1]}. We take a less conservative approach, allowing the decision-maker to express which nonstationary patterns are plausible, as in Example REF , or to view observable contexts as the only possible source of nonstationarity, as in Example REF . This kind of modeling flexibility is most interesting and valuable when motivated concretely by real world problems. One of the most exciting possibilities for future work is to test this approach with data arising from real experiments. In depth simulation experiments could also provide insight into sensitivity to model or prior mis-specification.
